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Open AccessArticle

An Enhanced Deep Convolutional Neural Network for Classifying Indian Classical Dance Forms

by Nikita Jain, Vibhuti Bansal, Deepali Virmani, Vedika Gupta, Lorenzo Salas-Morera and Laura Garcia-Hernandez

Appl. Sci. 2021, 11(14), 6253; https://doi.org/10.3390/app11146253 - 6 Jul 2021

Cited by 59 | Viewed by 7310

Abstract

Indian classical dance (ICD) classification is an interesting subject because of its complex body posture. It provides a stage to experiment with various computer vision and deep learning concepts. With a change in learning styles, automated teaching solutions have become inevitable in every field, from traditional to online platforms. Additionally, ICD forms an essential part of a rich cultural and intangible heritage, which at all costs must be modernized and preserved. In this paper, we have attempted an exhaustive classification of dance forms into eight categories. For classification, we have proposed a deep convolutional neural network (DCNN) model using ResNet50, which outperforms various state-of-the-art approaches. Additionally, to our surprise, the proposed model also surpassed a few recently published works in terms of performance evaluation. The input to the proposed network is initially pre-processed using image thresholding and sampling. Next, a truncated DCNN based on ResNet50 is applied to the pre-processed samples. The proposed model gives an accuracy score of 0.911. Full article

(This article belongs to the Special Issue Human-Computer Interaction for Industrial Applications)

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23 pages, 13484 KB

Open AccessArticle

A Deep Learning-Based End-to-End Composite System for Hand Detection and Gesture Recognition

by Adam Ahmed Qaid MOHAMMED, Jiancheng Lv and MD. Sajjatul Islam

Sensors 2019, 19(23), 5282; https://doi.org/10.3390/s19235282 - 30 Nov 2019

Cited by 68 | Viewed by 9729

Abstract

Recent research on hand detection and gesture recognition has attracted increasing interest due to its broad range of potential applications, such as human-computer interaction, sign language recognition, hand action analysis, driver hand behavior monitoring, and virtual reality. In recent years, several approaches have been proposed with the aim of developing a robust algorithm which functions in complex and cluttered environments. Although several researchers have addressed this challenging problem, a robust system is still elusive. Therefore, we propose a deep learning-based architecture to jointly detect and classify hand gestures. In the proposed architecture, the whole image is passed through a one-stage dense object detector to extract hand regions, which, in turn, pass through a lightweight convolutional neural network (CNN) for hand gesture recognition. To evaluate our approach, we conducted extensive experiments on four publicly available datasets for hand detection, including the Oxford, 5-signers, EgoHands, and Indian classical dance (ICD) datasets, along with two hand gesture datasets with different gesture vocabularies for hand gesture recognition, namely, the LaRED and TinyHands datasets. Here, experimental results demonstrate that the proposed architecture is efficient and robust. In addition, it outperforms other approaches in both the hand detection and gesture classification tasks. Full article

(This article belongs to the Special Issue Human-Machine Interaction and Sensors)

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